Vizualise Raw data
Report Author
07 décembre, 2021
mask_siteid_sampling <- site_protocol_quanti[
site_protocol_quanti$variable == "year" &
site_protocol_quanti$n >= 10,
]$siteid
mask_siteid_protocol <- site_protocol_quali[
site_protocol_quali$unitabundance %in% c("Count", "Ind.100m2"), ]$siteid
mask_siteid <- mask_siteid_sampling[mask_siteid_sampling %in% mask_siteid_protocol]trends_data <- abun_rich_op %>%
left_join(op_protocol, by = "op_id") %>%
filter(siteid %in% mask_siteid) %>%
mutate(
log_total_abundance = log(total_abundance),
log_species_nb = log(species_nb)
)plot_community_data <- function(dataset = NULL, y = NULL, x = NULL, title = NULL) {
p <- dataset %>%
ggplot(aes_string(y = y, x = x)) +
geom_point() +
geom_smooth(method = "loess", formula = "y ~ x")
if (!is.null(title)) {
p <- p +
labs(title = title)
}
return(p)
}
plot_trends <- trends_data %>%
group_by(siteid) %>%
nest() %>%
ungroup() %>%
slice_sample(n = 100) %>%
mutate(
p_abun = map2(data, siteid,
~plot_community_data(
dataset = .x, y = "total_abundance", x = "year", title = .y)),
p_rich = map2(data, siteid,
~plot_community_data(
dataset = .x, y = "species_nb", x = "year", title = .y),
)
)0.1 Total abundance
n_plot_by_batch <- 8
map(
split(
seq_len(nrow(plot_trends)),
1:floor(nrow(plot_trends) / n_plot_by_batch) + 1),
~plot_grid(plotlist = plot_trends[.x, ]$p_abun)
)
#> Warning in split.default(seq_len(nrow(plot_trends)), 1:floor(nrow(plot_trends)/
#> n_plot_by_batch) + : la taille de données n'est pas un multiple de la variable
#> découpée
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 2007
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 2
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 0
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
#> 2007
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
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0.2 Species richness
map(
split(
seq_len(nrow(plot_trends)),
1:floor(nrow(plot_trends) / n_plot_by_batch) + 1
),
~plot_grid(plotlist = plot_trends[.x, ]$p_rich)
)
#> Warning in split.default(seq_len(nrow(plot_trends)), 1:floor(nrow(plot_trends)/
#> n_plot_by_batch) + : la taille de données n'est pas un multiple de la variable
#> découpée
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : pseudoinverse used at 2007
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : neighborhood radius 2
#> Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
#> parametric, : reciprocal condition number 0
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
#> 2007
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2
#> Warning in predLoess(object$y, object$x, newx = if
#> (is.null(newdata)) object$x else if (is.data.frame(newdata))
#> as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
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0.3
tar_load(toy_dataset)
unique(toy_dataset$siteid)
#> [1] "S8633" "S11138" "S534" "S529" "S11219"
plot_temporal_biomass <- function (bm_data = NULL, biomass_var = NULL, com = NULL, .log = FALSE) {
#main_title <- paste0("Stab = ", round(1/(sync$cv_com), 2),", ", "Sync = ",
#round(sync$synchrony, 2),", ", "CVsp = ", round(sync$cv_sp, 2))
sym_bm_var <- rlang::sym(biomass_var)
# Total
total_biomass <- bm_data %>%
group_by(date) %>%
summarise(!!sym_bm_var := sum(!!sym_bm_var, na.rm = FALSE))
p <- bm_data %>%
mutate(label = if_else(date == max(date), as.character(species), NA_character_)) %>%
ggplot(aes_string(x = "date", y = biomass_var, color = "species")) +
geom_line() +
lims(y = c(0, max(total_biomass[[biomass_var]]))) +
labs(
#title = main_title, subtitle = paste0("Station: ", station),
y = "Biomass (g)", x = "Sampling date"
) +
ggrepel::geom_label_repel(aes(label = label),
size = 2.5, nudge_x = 1, na.rm = TRUE)
#Â Add total biomass
p2 <- p +
geom_line(data = total_biomass, aes(color = "black", size = 3)) +
theme(legend.position = "none")
# Add summary: richness, connectance, stab, t_lvl, sync, cv_sp
com %<>%
mutate_if(is.double, round(., 2))
label <- paste(
"S = ", com$bm_std_stab,
"sync = ", com$sync,
"CVsp = ", com$cv_sp,
"R = ", com$rich_tot_std,
"C = ", com$ct,
"Tlvl = ", com$t_lvl
)
p3 <- p2 +
annotate("text", x = median(total_biomass$date),
y = 15, label = label)
if (.log) {
p3 <- p3 + scale_y_log10()
}
return(p3)
}
ti <- toy_dataset %>%
filter(siteid == unique(toy_dataset$siteid)[2])
plot_population <- function (dataset = NULL, y_var = NULL, time_var = NULL) {
sym_y_var <- rlang::sym(y_var)
sym_time_var <- rlang::sym(time_var)
# Total
total_dataset <- dataset %>%
group_by(!!sym_time_var) %>%
summarise(!!sym_y_var := sum(!!sym_y_var, na.rm = FALSE))
p <- dataset %>%
mutate(label = if_else(!!sym_time_var == max(!!sym_time_var), as.character(species), NA_character_)) %>%
ggplot(aes_string(x = time_var, y = y_var, color = "species")) +
geom_line() +
lims(y = c(0, max(total_dataset[[y_var]]))) +
labs(
#title = main_title, subtitle = paste0("Station: ", station),
y = "Biomass (g)", x = "Sampling time_var"
) +
ggrepel::geom_label_repel(aes(label = label),
size = 2.5, nudge_x = 1, na.rm = TRUE)
#Â Add total biomass
p2 <- p +
geom_line(data = total_dataset, aes(color = "black", size = 3)) +
theme(legend.position = "none")
return(p2)
}
plot_population(dataset = ti, y_var = "abundance", time_var = "year")
#> Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
#' replace NA by 0 in abundance data
#'
#' @param dataset data.frame
#' @param y_var chr
#' @param species_var chr
#' @param time_var chr
#' @param long_format lgl
#'
#' @examples
#'tar_load(toy_dataset)
#'ti <- toy_dataset %>%
#' filter(siteid == unique(toy_dataset$siteid)[2])
#'get_com_matrix_from_site(dataset = ti, y_var = "abundance")
get_com_matrix_from_site <- function(
dataset = NULL,
y_var = NULL,
species_var = "species",
time_var = "year",
long_format = TRUE
) {
com <- dataset[, c(time_var, species_var, y_var)]
species <- unique(com[[species_var]])
com <- com %>%
pivot_wider(names_from = species_var, values_from = y_var) %>%
mutate(across(species, ~replace(.x, is.na(.x), 0))) %>%
arrange(!!sym(time_var)) %>%
complete(!!sym(time_var) := full_seq(!!sym(time_var), 1))
if (long_format) {
com <- com %>%
pivot_longer(cols = species, names_to = species_var, values_to = y_var)
}
return(com)
}
get_com_matrix_from_site(dataset = ti, y_var = "abundance")
#> Note: Using an external vector in selections is ambiguous.
#> ℹ Use `all_of(species_var)` instead of `species_var` to silence this message.
#> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
#> This message is displayed once per session.
#> Note: Using an external vector in selections is ambiguous.
#> ℹ Use `all_of(y_var)` instead of `y_var` to silence this message.
#> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
#> This message is displayed once per session.
#> Note: Using an external vector in selections is ambiguous.
#> ℹ Use `all_of(species)` instead of `species` to silence this message.
#> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
#> This message is displayed once per session.
#> # A tibble: 400 × 3
#> year species abundance
#> <dbl> <chr> <dbl>
#> 1 1997 Catostomus commersonii 63
#> 2 1997 Clinostomus funduloides 21
#> 3 1997 Etheostoma flabellare 14
#> 4 1997 Etheostoma olmstedi 20
#> 5 1997 Lepomis auritus 2
#> 6 1997 Lepomis gibbosus 0
#> 7 1997 Notropis hudsonius 0
#> 8 1997 Pimephales notatus 0
#> 9 1997 Rhinichthys atratulus 119
#> 10 1997 Semotilus atromaculatus 69
#> # … with 390 more rows
plot_temporal_population <- function(
com = NULL,
y_var = "abundance",
time_var = "year",
species_var = "species",
stacked = TRUE,
ribbon = FALSE,
.log = FALSE,
color_species = NULL,
label = NULL,
label_parsed = FALSE,
label_size = 4.5,
y_label = NULL,
my_ylim = NULL) {
# get bm dynamic
com <- get_com_matrix_from_site(
dataset = com,
y_var = y_var,
species_var = species_var,
time_var = time_var,
long_format = TRUE
)
# get total y
total <- com %>%
group_by(!!sym(time_var)) %>%
summarise(!!sym(y_var) != sum(!!sym(y_var)), .groups = "drop") %>%
mutate(!!sym(species_var) := "Total")
p <- com %>%
ggplot(aes_string(x = time_var, y = y_var, color = species_var))
if (stacked) {
if (ribbon) {
com <- arrange(com, !!sym(time_var), !!sym(species_var))
com$ymax <- com[[y_var]]
com$ymin <- 0
zl <- unique(com[[species_var]])
for (i in 2:length(zl)) {
zi <- com[[species_var]] == zl[i]
zi_1 <- com[[species_var]] == zl[i - 1]
com$ymin[zi] <- com$ymax[zi_1]
com$ymax[zi] <- com$ymin[zi] + com$ymax[zi]
}
p <- com %>%
ggplot(
aes_string(
x = time_var,
y = y_var,
ymax = "ymax",
ymin = "ymin",
fill = species_var)
) + geom_ribbon()
} else {
p <- p +
geom_area(aes_string(fill = species_var))
}
} else {
p <- p +
geom_line() +
geom_line(data = total, color = "black")
}
if (!is.null(my_ylim)) {
p <- p +
ylim(my_ylim)
}
# Make it professional:
p <- p +
labs(y = expression(Abundance), x = "Year")
# if (!is.null(sem_df)) {
# label <- get_network_summary(com = sem_df, station = station)
# }
if (!is.null(label)) {
if (is.null(y_label)) {
y_label <- max(total[[y_var]]) + max(total[[y_var]]) * 5 / 100
}
p <- p +
annotate("text", x = median(total[[year]]),
y = y_label,
label = label, parse = label_parsed, size = label_size)
}
if (.log) {
p <- p + scale_y_log10()
}
if (!is.null(color_species)) {
p <- p +
scale_fill_manual(values = color_species) +
scale_color_manual(values = color_species)
}
return(p)
}
plot_temporal_population(com = ti, ribbon = FALSE)
p <- plot_temporal_population(com = ti, ribbon = TRUE)
GeomRibbon$handle_na <- function(data, params) { data }
p$data %>%
ggplot(
aes(y = abundance, ymin = ymin, ymax = ymax, x = year,
fill = species)
) +
geom_ribbon()set.seed(1)
test <- data.frame(x = rep(1:10, 3), y = abs(rnorm(30)), z = rep(LETTERS[1:3],
10)) %>% arrange(x, z)
test[test$x == 4, "y"] <- NA
test$ymax <- test$y
test$ymin <- 0
zl <- unique(test$z)
for (i in 2:length(zl)) {
zi <- test$z == zl[i]
zi_1 <- test$z == zl[i - 1]
test$ymin[zi] <- test$ymax[zi_1]
test$ymax[zi] <- test$ymin[zi] + test$ymax[zi]
}
# fix GeomRibbon
GeomRibbon$handle_na <- function(data, params) { data }
ggplot(test, aes(x = x, y=y, ymax = ymax, ymin = ymin, fill = z)) +
geom_ribbon()- Be careful to multiple fishing per year
toy_dataset %>%
group_by(siteid, year, species) %>%
summarise(test=n()>1) %>%
filter(test)pop_trends <- toy_dataset %>%
filter(!siteid %in% c("S534", "S8633")) %>%
group_by(siteid) %>%
nest() %>%
mutate(
p_pop = map(data, ~plot_temporal_population(com = .x, ))
)
plot_grid(plotlist = pop_trends$p_pop)
#> Warning: Removed 280 rows containing missing values (position_stack).
#> Warning: Removed 104 rows containing missing values (position_stack).
0.4 Analysis
0.5 Reproducibility
Reproducibility receipt
## datetime
Sys.time()
#> [1] "2021-12-07 16:03:19 CST"
## repository
if(requireNamespace('git2r', quietly = TRUE)) {
git2r::repository()
} else {
c(
system2("git", args = c("log", "--name-status", "-1"), stdout = TRUE),
system2("git", args = c("remote", "-v"), stdout = TRUE)
)
}
#> Local: main /home/alain/Documents/post-these/isu/RivFishTimeBiodiversityFacets
#> Head: [861e78b] 2021-12-07: Raw data vizualisation
## session info
sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Debian GNU/Linux 10 (buster)
#>
#> Matrix products: default
#> BLAS: /home/alain/.Renv/versions/4.0.5/lib/R/lib/libRblas.so
#> LAPACK: /home/alain/.Renv/versions/4.0.5/lib/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
#> [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
#> [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] cowplot_1.1.1 rnaturalearthdata_0.1.0 rnaturalearth_0.1.0
#> [4] mapview_2.10.0 sf_0.9-7 rmarkdown_2.11
#> [7] scales_1.1.1 kableExtra_1.3.1 here_1.0.1
#> [10] magrittr_2.0.1 forcats_0.5.1 stringr_1.4.0
#> [13] dplyr_1.0.4 purrr_0.3.4 readr_2.1.1
#> [16] tidyr_1.1.2 tibble_3.1.6 ggplot2_3.3.3
#> [19] tidyverse_1.3.0 tarchetypes_0.3.2 targets_0.8.1
#> [22] conflicted_1.1.0 nvimcom_0.9-122
#>
#> loaded via a namespace (and not attached):
#> [1] leafem_0.1.6 colorspace_2.0-0 ellipsis_0.3.2 class_7.3-18
#> [5] leaflet_2.0.4.1 rprojroot_2.0.2 satellite_1.0.4 base64enc_0.1-3
#> [9] fs_1.5.1 rstudioapi_0.13 farver_2.0.3 ggrepel_0.9.1
#> [13] fansi_0.5.0 lubridate_1.7.9.2 xml2_1.3.2 codetools_0.2-18
#> [17] splines_4.0.5 cachem_1.0.4 knitr_1.36 jsonlite_1.7.2
#> [21] broom_0.7.4 dbplyr_2.1.0 png_0.1-7 compiler_4.0.5
#> [25] httr_1.4.2 backports_1.2.1 assertthat_0.2.1 Matrix_1.3-2
#> [29] fastmap_1.1.0 cli_3.1.0 htmltools_0.5.1.1 tools_4.0.5
#> [33] igraph_1.2.6 gtable_0.3.0 glue_1.5.1 Rcpp_1.0.6
#> [37] cellranger_1.1.0 jquerylib_0.1.3 raster_3.4-5 vctrs_0.3.8
#> [41] nlme_3.1-152 crosstalk_1.1.1 xfun_0.28 ps_1.6.0
#> [45] rvest_0.3.6 lifecycle_1.0.1 hms_1.1.1 yaml_2.2.1
#> [49] memoise_2.0.0 sass_0.3.1 stringi_1.7.6 highr_0.9
#> [53] e1071_1.7-4 rlang_0.4.12 pkgconfig_2.0.3 evaluate_0.14
#> [57] lattice_0.20-41 htmlwidgets_1.5.3 labeling_0.4.2 processx_3.5.2
#> [61] tidyselect_1.1.1 bookdown_0.24 R6_2.5.1 generics_0.1.0
#> [65] DBI_1.1.1 pillar_1.6.4 haven_2.3.1 withr_2.4.3
#> [69] mgcv_1.8-34 units_0.6-7 sp_1.4-5 modelr_0.1.8
#> [73] crayon_1.4.2 KernSmooth_2.23-18 utf8_1.2.2 tzdb_0.2.0
#> [77] grid_4.0.5 readxl_1.3.1 data.table_1.13.6 git2r_0.29.0
#> [81] callr_3.7.0 reprex_1.0.0 digest_0.6.27 classInt_0.4-3
#> [85] webshot_0.5.2 stats4_4.0.5 munsell_0.5.0 viridisLite_0.3.0
#> [89] bslib_0.2.4